COMP.4220 Machine Learning (Formerly 91.422)
Id: 008106
Credits: 3-3
Description
This introductory course gives an overview of machine learning techniques used in data mining and pattern recognition applications. Topics include: foundations of machine learning, including statistical and structural methods; feature discovery and selection; parametric and non-parametric classification; supervised and unsupervised learning; use of contextual evidence; clustering, recognition with strings; small sample-size problems and applications to large datasets.
Prerequisites
Pre-Reqs: COMP 1020 Computing II, MATH 3220 Discrete Structures ll and MATH 3860 Probability & Statistics I.
View Current Offerings
Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.